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(Appendix 3A) Scattergraph, High-Low Method, Method of Least Squares, Use of Judgment The management of Wheeler Company has decided to develop cost formulas for its

(Appendix 3A) Scattergraph, High-Low Method, Method of Least Squares, Use of Judgment

The management of Wheeler Company has decided to develop cost formulas for its major overhead activities. Wheeler uses a highly automated manufacturing process, and power costs are a significant manufacturing cost. Cost analysts have decided that power costs are mixed. The costs must be broken into their fixed and variable elements so that the cost behavior of the power usage activity can be properly described. Machine hours have been selected as the activity driver for power costs. The following data for the past 8 quarters have been collected:

Quarter / Machine Hours / Power Cost

1 20,000 $26,000

2 25,000 38,000

3 30,000 42,500

4 22,000 37,000

5 21,000 34,000

6 18,000 29,000

7 24,000 36,000

8 28,000 40,000

Note:For the following requirements, round the fixed cost to the nearest dollar, round the variable rates to three decimal places, and the R2to two decimal places.

Required:

1.Prepare a scattergraph by plotting power costs against machine hours. Does the scattergraph show a linear relationship between machine hours and power cost?

Yes

2.Using the high and low points (i.e., the high-low method), compute a power cost formula. (Note:Round variable rate to three decimal places.)

Total power cost = $___________+ ( $___________x Number of machine hours )

3.Use the method of least squares to compute a power cost formula. Evaluate the coefficient of determination.

Variable rate (to two decimal places)$___________ per machine hour

Fixed cost (to the nearest dollar)$_________

Coefficient of determination (R2) (to one decimal place). _________%

4.Conceptual Connection: Rerun the regression, and drop the point (20,000, $26,000) as an outlier. Compare the results from this regression to those for the regression in Requirement 3. Which is better?

This regression looks better in terms of R squared.

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